Data Lake Tools & Solutions to Unlock Insights

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Data lakes are useful storage sites for companies of all sizes and industries. They're incredibly versatile, holding both structured and unstructured data. However, companies must search out solutions to keep data lake assets organized, optimized, and secure.

What are data lake solutions?

A data lake is a central repository for storing different types of data from various different sources. Data lake solutions refer to the different types of data lakes available (e.g., on-prem, cloud, hybrid) and the vendors that offer them. It can also refer to the different pieces that together create the architecture of a data lake (e..g, the metadata layer, query layer, etc.).  

Below, we take a closer look at four different solutions that play an instrumental part in constructing and scaling a data lake. 

4 data lake tools to unlock customer insights

Two popular data lake options are Amazon Web Services (AWS) S3 and Azure Data Lake Storage (ADLS) Gen2, which serve as the storage layer within a data lake. Segment is compatible with both these solutions – able to send consolidated data in an optimized format to reduce processing times.  

Let’s learn more about the different tools used at each layer in a data lake. (As a reminder, this isn’t an exhaustive list, but a compilation of popular solutions.) 

AWS Glue

The AWS Glue Data Catalog is a central store for all the metadata in your data lake (e.g., different data types, formats, locations, etc.). 

Once a data event occurs, it gets assigned a schema and becomes part of the data tables and columns inside the AWS Glue Data Catalog. Once it’s been cataloged into a more usable format, various data analytics applications can find it easier and faster, and your business tools become more powerful than before.  

Athena 

Athena provides a SQL interface to query data in your data lake (in Amazon S3). Athena is serverless and customers only pay-per-query. 

Source

Athena allows you to query data stored in common file formats like CSV, TSV (Tab-Separated Values), and Parquet, helping to filter, aggregate, and analyze data within these files. 

Other potential solutions for the query layer in a data lake include BigQuery or Apache Hive. 

EMR

Elastic MapReduce (EMR) is a cloud-based big data processing service provided by AWS. It simplifies the processing of large datasets with the help of frameworks like Apache Hadoop and Apache Spark. 

EMR makes it easier for businesses to perform data transformation, analysis, and machine learning by providing a managed and scalable environment for running distributed data processing applications. 

Advanced ML and AI: Databricks

The partnership between Segment and Databricks offers a two-way transmission of data between tools for advanced analytics and AI insights. Use Segment and Databricks together to collect and activate data from inside your data lake. Some of the benefits include: 

• Activating data from Databricks Lakehouse in over 450 Segment-supported destinations (as well as any custom integrations). 

• Build powerful AI models on enriched, golden customer profiles (with less tools and simplified data orchestration). 

   

Segment and Databricks partnership

How to choose the right data lake tool for your needs

Choosing the right data lake solution(s) will depend on your business and intended use cases. However, there are a few overarching pieces of advice worth following, from considering how well it will integrate within your existing data architecture to data security. 

Find one that integrates with your existing data architecture 

Data lakes are part of a larger ecosystem of tools which range from analytics to data processing and machine learning. When choosing a data lake, it’s important to think through how well it will integrate with the existing tools in your tech stack. 

Check which data formats your top choices support

Data lakes often support various file formats, such as CSV, JSON, and Parquet.  But data format may impact the data lake’s ease and efficiency when it comes to processing and querying.  For example, a data lake optimized for columnar storage might prefer Parquet for better performance.

Verify its security capabilities

Look for data lake vendors that understand the current state of cybersecurity and what’s required for data in your particular industry. A few things to consider when it comes to data safety include:

• Data encryption

• Risk monitoring

• Access controls

Analyze your budget

Data lake solutions rarely have a single upfront cost; their prices usually fluctuate based on each customer’s unique needs. The final cost will depend on your data volume, storage needs, features, integrations, and setup or training fees.  

Turnkey data lake solutions with Segment

Segment's out-of-the-box data lake balances efficiency, cost, and performance. Engineers get the most out of existing data while positioning new data for future analytics and machine learning capabilities. Here are a few benefits of using Segment Data Lakes. 

No need to build from the ground up

Data lakes are incredibly useful, but creating your own from the ground up is a considerable task – one that can take months on end. By using Segment’s baseline data architecture, engineers skip costly steps and have an easier time maintaining the data lake down the road. 

Segment also applies a standard schema so it’s easier (and quicker) to query raw data. (You can learn more about Segment’s schema and how it partitions data for S3 and ALDS here.) 

More affordable storage and processing

Cost will always be a significant consideration for businesses. Because data lakes run on affordable data stores like Amazon S3, they are much more reasonable to build, use, and maintain. Take Rokfin, which was able to reduce their data storage costs by 60% when they migrated from a data warehouse to Segment Data Lakes.

In the past, cost concerns and architecture constraints may have forced businesses into only keeping subsets of their data or risk higher storage fees and less than optimal compute performance. But with Segment Data Lakes, all historical and current behavioral data can be stored in a cost-effective and efficient way. Teams can also use Segment replay to re-process and re-ingest historical data they’d previously unloaded, to rebuild a behavioral dataset in S3.